Collaborative phenotyping effort of European Drosophila melanogaster populations
Genomic analyses (Kapun et al. 2020) showed that there is longitudinal population structure, continent-wide sweeps, candidate genes for local climate adaptation by using 48 pooled population samples from 32 locations.
In 2017, DrosEU consortium has decided to make a collaborative phenotyping effort of European Drosophila melanogaster populations at the Groningen meeting.
The call was made with approximate numbers of populations, lines, etc. and under predefined criteria (finish within a year, reps, blocks, etc) and participating labs have indicated which traits they are willing to phenotype.
Nine sampling locations were chosen based on genomic data and cover a wide range of latitude (~20°) and longitude (~40°) across the continent (see table below). From each location, 15 to 20 isofemale lines were established in corresponding labs at the sampling location and the isofemale lines were centrally maintained by Élio Sucena at Instituto Gulbenkian de Ciência (IGC), Lisbon, Portugal. A total of 173 isofemale lines were used in this study.
| Country | Location | Latitude | Longitude | Altitude | Collector |
|---|---|---|---|---|---|
| Portugal | Recarei | 41.150 | -8.410 | 175 | Jorge Vieira |
| Spain | Gimenells (Lleida) | 41.618 | 0.620 | 173 | Josefa Gonzalez |
| Denmark | Karensminde | 55.945 | 10.213 | 15 | Mads Schou |
| Germany | Munich | 48.180 | 11.610 | 520 | Amanda Glaser-Schmitt |
| Austria | Mauternbach | 48.375 | 15.560 | 572 | Andrea Betancourt |
| Finland | Akaa | 61.100 | 23.520 | 88 | Maaria Kankare |
| Ukraine | Uman | 48.753 | 30.206 | 214 | Iryna Kozeretska |
| Turkey | Yesiloz | 40.231 | 32.260 | 680 | Banu Onder |
| Russia | Valday | 57.979 | 33.244 | 217 | Elena Pasyukova |
## Scale on map varies by more than 10%, scale bar may be inaccurate
Contributed labs are Abbott, Bergland, Billeter, Colinet, Flatt, Fricke, Gibert, Gonzalez, Grath, Hoedjes, Kozeretska, Mensch, Onder, Parsch, Pasyukova, Posnien, Ritchie, Schlötterer, Schmidt, Stamenkovic-Radak, Tauber, Vieira, Wegener, Zwaan and detailed list below :
| Country | Lab | PI | Trait |
|---|---|---|---|
| Sweden | Lund | Abbott | Pigmentation |
| USA | Charlottesville | Bergland | Diapause |
| The Netherlands | Groningen | Billeter | Fecundity |
| France | Rennes | Colinet | Dry weight |
| Switzerland | Fribourg | Flatt | Diapause |
| Switzerland | Fribourg | Flatt | Lifespan |
| Germany | Muenster | Fricke | Fecundity |
| France | Lyon | Gibert | Development time |
| France | Lyon | Gibert | Pigmentation |
| France | Lyon | Gibert | Viability |
| Spain | Barcelona | Gonzalez | Cold-shock mortality |
| Spain | Barcelona | Gonzalez | Starvation resistance |
| Germany | Munich | Grath | Development time |
| Germany | Munich | Grath | Viability |
| The Netherlands | Lausanne | Hoedjes | Development time |
| The Netherlands | Lausanne | Hoedjes | Dry weight |
| The Netherlands | Lausanne | Hoedjes | Viability |
| Ukraine | Kyiv | Kozeretska | Cold-shock mortality |
| Ukraine | Kyiv | Kozeretska | Thorax length |
| Argentina | Buenos Aiers | Mensch | Chill-coma recovery time |
| Turkey | Ankara | Onder | Dry weight |
| Turkey | Ankara | Onder | Starvation resistance |
| Turkey | Ankara | Onder | Wing area |
| Germany | Munich | Parsch | Heat-shock mortality |
| Germany | Munich | Parsch | Lifespan |
| Russia | Moscow | Pasyukova | Lifespan |
| Russia | Moscow | Pasyukova | Starvation resistance |
| Germany | Göttingen | Posnien | Thorax length |
| Germany | Göttingen | Posnien | Wing area |
| UK | St Andrews | Ritchie | Thorax length |
| UK | St Andrews | Ritchie | Wing area |
| UK | St Andrews | Ritchie | Wing patterning |
| Austria | Vienna | Schlötterer | Diapause |
| USA | Philadelphia | Schmidt | Development time |
| USA | Philadelphia | Schmidt | Pigmentation |
| USA | Philadelphia | Schmidt | Thorax length |
| USA | Philadelphia | Schmidt | Time to pupation |
| USA | Philadelphia | Schmidt | Viability |
| Serbia | Belgrade | Stamenkovic-Radak | Development time |
| Serbia | Belgrade | Stamenkovic-Radak | Viability |
| Serbia | Belgrade | Stamenkovic-Radak | Wing area |
| Israel | Haifa | Tauber | Locomotor activity |
| Portugal | Porto | Vieira | Chill-coma recovery time |
| Portugal | Porto | Vieira | Cold-shock mortality |
| Portugal | Porto | Vieira | Heat-shock mortality |
| Germany | Würzburg | Wegener | Circadian eclosion timing |
| The Netherlands | Wageningen | Zwaan | Development time |
| The Netherlands | Wageningen | Zwaan | Viability |
Esra Durmaz (all), Envel Kerdaffrec (data check, data clean-up, data reformatting, LS, Dia, and others), Katja Hoedjes (DW, Via, DT), Banu Onder (SR, WA), Chris Wegener (CET), Eran Tauber (LA), Rudolf Rohr (UniFr), coordination-team
Contributors and their experimental methods
3-5 day old adults (at least 25 pairs) are allowed to lay eggs en masse. Yeast is provided to stimulate egg laying (for at least 2 hours). Eggs are collected, and 40 are placed in each vial(^*^). Viability is calculated per vial, as the percentage of individuals that emerged from the 40 eggs.
Please see below number of
^*^ @ Schmidt lab, females were allowed to lay eggs for Xh, and viability is calculated as the percentage of individuals that emerged from total number of eggs, per vial.
Developmental time is scored as both the egg-to-pupa and egg-to-adult development time. Both were scored twice a day, when the chamber lights are turned on and two hours before they are turned off. In order to measure the egg-to-pupa developmental time, the spot where a pupa is found is marked with a permanent marker to keep track of which pupae have emerged in each day. The egg-to-adult developmental time is estimated by counting all emerged adults from the vial, and by sexing them.
At day 7 after emergence, flies are killed by snap freezing them in liquid nitrogen, by putting them at -20ºC or by putting them into an ethyl acetate solution and stored at -20ºC. Then they are sexed and placed into 96 wells plates, and placed in an oven set at 60-70 °C, for at least 3 days. At this point flies can be stored at room temperature using a protective cover. If this is the case, the day before measurements are made dry flies are again placed for 24h in the oven (60-70 °C) to ensure material is well dehydrated. Flies are then placed on a small piece of aluminium foil for direct weight measurement on microbalance (accuracy 1µg).
Five to seven days old flies are placed onto a double-sided sticky tape attached to a microscope slide and a picture of the thorax taken using a digital camera connected to a dissecting microscope. The same magnification and resolution is always used to increase reproducibility, and a scale bar inserted on each photo to allow transforming pixels into µm units. Thorax length is defined as the distance from the anterior margin of the thorax to the posterior tip of the scutellum and it is measured using the “Straight Line” in ImageJ/Fiji.
Both the left and right wings of five to seven days old flies (10 flies per sex per replicate) are removed and placed into a drop of Entellan®Merck, Hoyer’s Medium, sticked to a double-side sticky tape, or taped directly to the slide. Pictures of the wing preparations are taken using a digital camera connected to a dissecting microscope. The same magnification and resolution is always used to increase reproducibility. A scale bar is placed on each photo to allow transforming pixels into µm units. Manual measurements of wing length and wing area are performed using the “Straight Line” and “Polygon Selection” tools, respectively of ImageJ/Fiji (10.1371/journal.pone.0000007).
For each isofemale line, 10 males and 10 females are placed together in single-sex groups and allowed to mature for five days. Then, they are placed together (5-7 pairs), and mating interactions observed to ensure successful mating (at least 10 min copulation duration) to ensure that we have five successfully mated females. After a successful mating, males are discarded and females allowed to oviposit alone for 48 hours, moved to another vial, and allowed to oviposit for four days, and again moved to another vial and allowed to oviposit for two days to check that of egg-laying stopped. Vials are incubated until all offspring is born. Individuals are then frozen and the offspring counted.
Line level lifespan: Ten flies per sex/line are placed in each vial. The age at death will be scored when changing the food, at least three times a week. Five replicates are used.
Population level lifespan: Flies are kept in 1L demography cages (5 flies per line/sex for each population). The age at death will be scored when changing the food, at least three times a week. Ten replicates were performed.
Batches of 15-20 seven days old flies are placed for 18 hours in an empty vial immersed in an ice-water slurry box placed at a 4°C room for 18 hours. Then the vials are removed to a bench in a 25°C room and mortality scored 24 hours later.
Sexed flies are placed in an empty vial immersed in an ice-water slurry box placed at a 4°C room in the morning. Six hours later, flies are removed from the tube to individual wells of 24 well plates while being kept on ice. A timer is started once the plate is moved from the ice to a bench in a 25°C room. Each fly is checked by eye for recovery for a maximum of 60 minutes. Flies that are able to stand on their legs are considered recovered and the CCRT (in seconds) recorded.
Batches of 15-20 seven days old flies are placed in empty vials inside a 37ºC incubator and mortality checked for 7 hours every 30 minutes.
In order to induce diapause, two hour old ‘phenotypic virgins’ (pharate or melanized with meconium visible) female flies are exposed to 12°C and 10:14 light/dark hours for 3 weeks, using an incubator that allows temperature tracking. Vials are changed once per week. After three weeks under diapause conditions, flies are frozen at -80C until dissection. Both ovaries will be examined and classified according to the following simplified ‘classification’: 1) < stage 10: diapause; 2) stage 10-13: intermediate; 3) stage 14: non-diapause.
The locomotor activity of the flies (males only) was measured using the DAM2 Drosophila monitors (Trikinetics Inc., Waltham, MA). Flies were 1-3 days old. Single flies were placed in glass tubes (10 cm × 0.5 cm) that were filled with 2 cm sugar/agar medium. The monitors were placed in light chambers driven by LED, in an incubator at 24°C, ~30% humidity. The flies were entrained to a light-dark cycle (LD 12:12) for 5d and then allowed to free-run for 10 d in constant darkness (DD). The activity data were processed into 30 min bins, and four different variables were analysed. These included the circadian period, and the phase, which were analyzed using the FFT NLLS algorithm available at the BioDare2 server (https://biodare2.ed.ac.uk/). The other two variables, level of activity and the nocturnal/diurnal ratio were analyzed by a custom-made R script.
Batches of 10 sexed flies are transferred to glass vials filled with 5 mL of 2% agar for starvation 3-7 days after eclosion. The age at death will be scored every 8 hours.
For each line, 10 females, 13-15 days old, either alive or stored in 95% ethanol, are air dried and placed on their left side, and pictures taken using a dissecting microscope. Images are then analysed in ImageJ 1.46r, using the Area Fraction measurement in the Analyze menu. Area Fraction measures the percentage of pixels in a selected area that have been highlighted in red using the Threshold tool. This gives an estimate of the percentage of dark pigmentation on the three terminal tergites of the abdomen (4, 5 and 6).
MasterSheets
Insert a MasterSheet example
For each trait, sex and lab, we run counts for population, line and replicate vial (if applicable).
“Plots and Linear Models by Lab” are presented in alphabetical order.
setwd(localgit)
Contributors:
Gibert Lab :Cristina Vieira, Laurence Mouton, Natacha Kremer, Sonia Martinez, Patricia Gibert
Grath Lab : Ingo Müller, Sonja Grath
Hoedjes Lab : Hristina Kostic, Katja Hoedjes
Schmidt Lab : Ozan Kiratli, Yonatan Babore, Liam Forsythe, Paul Schmidt
Stamenkovic-Radak Lab : Marija Savic Veselinovic, Marija Tanaskovic, Aleksandra Patenkovic, Mihailo Jelic, Katarina Eric, Pavle Eric, Slobodan Davidovic, Marina Stamenkovic-Radak
Zwaan Lab : Joost van den Heuvel, Bas Zwaan
Reading data in R
d_Via <- read.csv("MasterSheets_Oct21_git/VIA_MasterSheet_Oct21.csv")
str(d_Via)
## 'data.frame': 2367 obs. of 12 variables:
## $ Supervisor.PI : chr "Gibert" "Gibert" "Gibert" "Gibert" ...
## $ Diet : chr "NS" "NS" "NS" "NS" ...
## $ Batch : int 1 1 1 1 1 1 1 1 1 1 ...
## $ Population : chr "AK" "AK" "AK" "AK" ...
## $ Line : chr "AK1" "AK1" "AK1" "AK10" ...
## $ ReplicateVialOld : int 1 2 3 1 2 3 1 2 3 1 ...
## $ ReplicateVial : chr "Gibert_1_AK1_1" "Gibert_1_AK1_2" "Gibert_1_AK1_3" "Gibert_1_AK10_1" ...
## $ ProportionEggtoAdultSurvival: num 0.68 0.73 0.63 0.85 0.75 0.8 0.85 0.88 0.7 0.68 ...
## $ Country : chr "Finland" "Finland" "Finland" "Finland" ...
## $ Latitude : num 61.1 61.1 61.1 61.1 61.1 61.1 61.1 61.1 61.1 61.1 ...
## $ Longitude : num 23.5 23.5 23.5 23.5 23.5 ...
## $ Altitude : int 88 88 88 88 88 88 88 88 88 88 ...
Factors need reformatting (i.e. Supervisor.PI should be coded as a factor, not character).
str(d_Via)
## 'data.frame': 2367 obs. of 12 variables:
## $ Supervisor.PI : chr "Gibert" "Gibert" "Gibert" "Gibert" ...
## $ Diet : chr "NS" "NS" "NS" "NS" ...
## $ Batch : int 1 1 1 1 1 1 1 1 1 1 ...
## $ Population : chr "AK" "AK" "AK" "AK" ...
## $ Line : chr "AK1" "AK1" "AK1" "AK10" ...
## $ ReplicateVialOld : int 1 2 3 1 2 3 1 2 3 1 ...
## $ ReplicateVial : chr "Gibert_1_AK1_1" "Gibert_1_AK1_2" "Gibert_1_AK1_3" "Gibert_1_AK10_1" ...
## $ ProportionEggtoAdultSurvival: num 0.68 0.73 0.63 0.85 0.75 0.8 0.85 0.88 0.7 0.68 ...
## $ Country : chr "Finland" "Finland" "Finland" "Finland" ...
## $ Latitude : num 61.1 61.1 61.1 61.1 61.1 61.1 61.1 61.1 61.1 61.1 ...
## $ Longitude : num 23.5 23.5 23.5 23.5 23.5 ...
## $ Altitude : int 88 88 88 88 88 88 88 88 88 88 ...
d_Via$Supervisor.PI <- as.factor(d_Via$Supervisor.PI)
d_Via$Diet <- as.factor(d_Via$Diet)
d_Via$Batch <- as.factor(d_Via$Batch)
d_Via$Population_Lat <- factor(d_Via$Population, levels= c("YE","RE","GI","MU","MA","UM","KA","VA","AK"))
d_Via$Population_Lon <- factor(d_Via$Population, levels= c("RE","GI","KA","MU","MA","AK","UM","YE","VA"))
d_Via$Population_Alt <- factor(d_Via$Population, levels= c("KA","AK","GI","RE","UM","VA","MU","MA","YE"))
d_Via$Line <- as.factor(d_Via$Line)
d_Via$ReplicateVial <- as.factor(d_Via$ReplicateVial)
d_Via$ProportionEggtoAdultSurvival <- as.numeric(d_Via$ProportionEggtoAdultSurvival)
d_Via$Country <- as.factor(d_Via$Country)
d_Via$Latitude <- as.numeric(d_Via$Latitude)
d_Via$Longitude <- as.numeric(d_Via$Longitude)
d_Via$Altitude <- as.numeric(d_Via$Altitude)
# Now they should be in the correct format, see below.
str(d_Via)
## 'data.frame': 2367 obs. of 15 variables:
## $ Supervisor.PI : Factor w/ 6 levels "Gibert","Grath",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ Diet : Factor w/ 1 level "NS": 1 1 1 1 1 1 1 1 1 1 ...
## $ Batch : Factor w/ 4 levels "1","2","3","4": 1 1 1 1 1 1 1 1 1 1 ...
## $ Population : chr "AK" "AK" "AK" "AK" ...
## $ Line : Factor w/ 172 levels "AK1","AK10","AK11",..: 1 1 1 2 2 2 4 4 4 9 ...
## $ ReplicateVialOld : int 1 2 3 1 2 3 1 2 3 1 ...
## $ ReplicateVial : Factor w/ 2367 levels "Gibert_1_AK1_1",..: 1 2 3 4 5 6 7 8 9 10 ...
## $ ProportionEggtoAdultSurvival: num 0.68 0.73 0.63 0.85 0.75 0.8 0.85 0.88 0.7 0.68 ...
## $ Country : Factor w/ 9 levels "Austria","Denmark",..: 3 3 3 3 3 3 3 3 3 3 ...
## $ Latitude : num 61.1 61.1 61.1 61.1 61.1 61.1 61.1 61.1 61.1 61.1 ...
## $ Longitude : num 23.5 23.5 23.5 23.5 23.5 ...
## $ Altitude : num 88 88 88 88 88 88 88 88 88 88 ...
## $ Population_Lat : Factor w/ 9 levels "YE","RE","GI",..: 9 9 9 9 9 9 9 9 9 9 ...
## $ Population_Lon : Factor w/ 9 levels "RE","GI","KA",..: 6 6 6 6 6 6 6 6 6 6 ...
## $ Population_Alt : Factor w/ 9 levels "KA","AK","GI",..: 2 2 2 2 2 2 2 2 2 2 ...
# Voila!
Tables for descriptive statistics at population and line level can be found in the main trait directory, under the file name table_TraitAbbreviation_Level_BatchInfo.csv (i.e. table_Via_Line_wobatch.csv)
Descriptive statistics for viability at the line level, with batch information :
Descriptive statistics for viability at the line level, without batch information :
Descriptive statistics for viability at the population level, with batch information :
Descriptive statistics for viability at the population level, without batch information :
The very same theme has been used all across the document, being a ggplot theme called droseu_theme
droseu_theme <- theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(), panel.background = element_blank(), axis.line = element_line(colour = "black",), axis.title.x = element_text(size = 16), axis.text.x = element_text(size = 16),axis.text.y = element_text(size = 16),axis.title.y = element_text(size = 16))
Data range is calculated with #r min() and #r max() functions for each trait.
min_Via <- min(d_Via$ProportionEggtoAdultSurvival)
max_Via <- max(d_Via$ProportionEggtoAdultSurvival)
y-axis is scaled by the minimum (0) and maximum (1) values in the full data set.
Here is an example code for egg-to-adult viability, Gibert Lab. The same code is used to generate plots for other contributing labs by filtering the data at supervisor level (for females and males, if applicable).
pdf(file="Viability/p_Via_Gibert.pdf",width=8, height=5)
p_Via_Gibert <- ggplot(data = (subset(d_Via,Supervisor.PI=='Gibert')), aes(x=Population_Lat, y=ProportionEggtoAdultSurvival, fill=Batch)) + geom_boxplot(outlier.shape = NA, notch=FALSE) + geom_jitter(width=0.2,alpha=0.05) + labs(title="p_Via_Gibert", x="Population", y = "ProportionEggtoAdultSurvival")
p_Via_Gibert + coord_cartesian(ylim = c(0, 1))+ droseu_theme
invisible(dev.off())
pdf(file="Viability/p_Via_pop_Gibert.pdf",width=8, height=5)
p_Via_pop_Gibert <- ggplot(data = (subset(d_Via,Supervisor.PI=='Gibert')), aes(x=Population_Lat, y=ProportionEggtoAdultSurvival, fill=Population)) +geom_boxplot(outlier.shape = NA, notch=FALSE) + geom_jitter(width=0.2,alpha=0.05) + labs(title="p_Via_pop_Gibert", x="Population", y = "ProportionEggtoAdultSurvival")
p_Via_pop_Gibert + coord_cartesian(ylim = c(0, 1))+ droseu_theme
invisible(dev.off())
Via_lmer_Gibert <- lmer(ProportionEggtoAdultSurvival ~ Population + (1|Line:Population) + (1|Batch) , data = d_Via[d_Via$Supervisor.PI == "Gibert",])
capture.output(summary(Via_lmer_Gibert),file = "Viability/Via_lmer_Gibert_sum.txt")
capture.output(anova(Via_lmer_Gibert),file = "Viability/Via_lmer_Gibert.txt")
capture.output(emmeans(Via_lmer_Gibert, list(pairwise ~ Population), adjust = "tukey"),file = "Viability/Via_lmer_Gibert_tk.txt")
anova(Via_lmer_Gibert)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Population 0.6029 0.075362 8 154.72 8.1983 3.224e-09 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summary(Via_lmer_Gibert)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: ProportionEggtoAdultSurvival ~ Population + (1 | Line:Population) +
## (1 | Batch)
## Data: d_Via[d_Via$Supervisor.PI == "Gibert", ]
##
## REML criterion at convergence: -687.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.8895 -0.5220 0.0038 0.5549 2.4861
##
## Random effects:
## Groups Name Variance Std.Dev.
## Line:Population (Intercept) 1.064e-02 0.103146
## Batch (Intercept) 6.195e-05 0.007871
## Residual 9.192e-03 0.095877
## Number of obs: 532, groups: Line:Population, 169; Batch, 3
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.75693 0.02658 51.26227 28.476 < 2e-16 ***
## PopulationGI -0.14773 0.03986 155.28178 -3.706 0.000292 ***
## PopulationKA -0.03422 0.03696 155.95970 -0.926 0.355981
## PopulationMA -0.11278 0.03691 154.48413 -3.056 0.002647 **
## PopulationMU -0.03629 0.03692 155.28351 -0.983 0.327104
## PopulationRE -0.11002 0.03921 155.98273 -2.806 0.005655 **
## PopulationUM -0.04241 0.03792 155.09608 -1.118 0.265136
## PopulationVA -0.12079 0.03692 155.28351 -3.272 0.001316 **
## PopulationYE -0.24601 0.03692 155.28351 -6.664 4.38e-10 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) PpltGI PpltKA PpltMA PpltMU PpltRE PpltUM PpltVA
## PopulatinGI -0.640
## PopulatinKA -0.691 0.462
## PopulatinMA -0.693 0.463 0.500
## PopulatinMU -0.692 0.463 0.500 0.500
## PopulatinRE -0.651 0.437 0.470 0.470 0.471
## PopulatinUM -0.673 0.451 0.487 0.487 0.487 0.458
## PopulatinVA -0.692 0.463 0.500 0.500 0.500 0.471 0.487
## PopulatinYE -0.692 0.463 0.500 0.500 0.500 0.471 0.487 0.500
anova(Via_lmer_Grath)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Population 0.07035 0.035175 2 27.293 2.1926 0.1309
summary(Via_lmer_Grath)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: ProportionEggtoAdultSurvival ~ Population + (1 | Line:Population)
## Data: d_Via[d_Via$Supervisor.PI == "Grath", ]
##
## REML criterion at convergence: -155.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.14211 -0.56745 0.04161 0.55412 2.60033
##
## Random effects:
## Groups Name Variance Std.Dev.
## Line:Population (Intercept) 0.003394 0.05826
## Residual 0.016043 0.12666
## Number of obs: 147, groups: Line:Population, 30
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.69027 0.02612 28.41694 26.424 <2e-16 ***
## PopulationMU -0.07424 0.03664 27.57805 -2.026 0.0525 .
## PopulationRE -0.05444 0.03664 27.57805 -1.486 0.1487
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) PpltMU
## PopulatinMU -0.713
## PopulatinRE -0.713 0.508
anova(Via_lmer_Hoedjes)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Population 0.35451 0.044314 8 158 6.1992 5.871e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summary(Via_lmer_Hoedjes)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: ProportionEggtoAdultSurvival ~ Population + (1 | Line:Population) +
## (1 | Batch)
## Data: d_Via[d_Via$Supervisor.PI == "Hoedjes", ]
##
## REML criterion at convergence: -712.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.7848 -0.5126 -0.0026 0.5498 2.7446
##
## Random effects:
## Groups Name Variance Std.Dev.
## Line:Population (Intercept) 1.219e-02 1.104e-01
## Batch (Intercept) 2.196e-12 1.482e-06
## Residual 7.148e-03 8.455e-02
## Number of obs: 501, groups: Line:Population, 167; Batch, 4
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.77450 0.02699 157.99983 28.694 < 2e-16 ***
## PopulationGI -0.17183 0.04123 157.99984 -4.168 5.06e-05 ***
## PopulationKA -0.05633 0.03817 157.99984 -1.476 0.141995
## PopulationMA -0.06600 0.03817 157.99984 -1.729 0.085761 .
## PopulationMU -0.03500 0.03817 157.99984 -0.917 0.360593
## PopulationRE -0.15117 0.04123 157.99985 -3.666 0.000336 ***
## PopulationUM -0.08960 0.03982 157.99985 -2.250 0.025827 *
## PopulationVA -0.09367 0.03817 157.99984 -2.454 0.015222 *
## PopulationYE -0.21483 0.03817 157.99984 -5.628 8.11e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) PpltGI PpltKA PpltMA PpltMU PpltRE PpltUM PpltVA
## PopulatinGI -0.655
## PopulatinKA -0.707 0.463
## PopulatinMA -0.707 0.463 0.500
## PopulatinMU -0.707 0.463 0.500 0.500
## PopulatinRE -0.655 0.429 0.463 0.463 0.463
## PopulatinUM -0.678 0.444 0.479 0.479 0.479 0.444
## PopulatinVA -0.707 0.463 0.500 0.500 0.500 0.463 0.479
## PopulatinYE -0.707 0.463 0.500 0.500 0.500 0.463 0.479 0.500
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see ?isSingular
## quartz_off_screen
## 2
anova(Via_lm_Schmidt) #lm()` is used only for Schmidt Lab's viability data, as only one vial per line was phenotyped
## Analysis of Variance Table
##
## Response: ProportionEggtoAdultSurvival
## Df Sum Sq Mean Sq F value Pr(>F)
## Population 8 1.0046 0.125579 2.5425 0.01256 *
## Residuals 153 7.5570 0.049392
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summary(Via_lm_Schmidt)
##
## Call:
## lm(formula = ProportionEggtoAdultSurvival ~ Population, data = d_Via[d_Via$Supervisor.PI ==
## "Schmidt", ])
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.48129 -0.19237 0.02616 0.16173 0.51871
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.625157 0.049695 12.580 <2e-16 ***
## PopulationGI -0.143866 0.075911 -1.895 0.0600 .
## PopulationKA 0.135186 0.070280 1.924 0.0563 .
## PopulationMA 0.007477 0.072206 0.104 0.9177
## PopulationMU -0.052544 0.070280 -0.748 0.4558
## PopulationRE 0.004666 0.075911 0.061 0.9511
## PopulationUM 0.085505 0.077444 1.104 0.2713
## PopulationVA -0.041436 0.070280 -0.590 0.5563
## PopulationYE -0.087422 0.070280 -1.244 0.2154
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.2222 on 153 degrees of freedom
## Multiple R-squared: 0.1173, Adjusted R-squared: 0.07119
## F-statistic: 2.542 on 8 and 153 DF, p-value: 0.01256
anova(Via_lmer_StamenkovicRadak)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Population 0.39276 0.049095 8 155.26 5.0862 1.243e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summary(Via_lmer_StamenkovicRadak)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: ProportionEggtoAdultSurvival ~ Population + (1 | Line:Population) +
## (1 | Batch)
## Data: d_Via[d_Via$Supervisor.PI == "StamenkovicRadak", ]
##
## REML criterion at convergence: -589.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.6754 -0.5185 0.0052 0.5759 2.2517
##
## Random effects:
## Groups Name Variance Std.Dev.
## Line:Population (Intercept) 0.013176 0.11479
## Batch (Intercept) 0.001207 0.03475
## Residual 0.009652 0.09825
## Number of obs: 501, groups: Line:Population, 167; Batch, 4
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.67693 0.03355 21.98005 20.176 1.13e-15 ***
## PopulationGI -0.07436 0.04570 155.38082 -1.627 0.10573
## PopulationKA -0.02793 0.04054 155.23842 -0.689 0.49191
## PopulationMA -0.12034 0.04051 155.10198 -2.971 0.00344 **
## PopulationMU 0.02403 0.04051 155.08224 0.593 0.55386
## PopulationRE -0.05090 0.04302 155.34042 -1.183 0.23859
## PopulationUM -0.08775 0.04168 155.34524 -2.106 0.03685 *
## PopulationVA -0.11615 0.04051 155.10172 -2.867 0.00472 **
## PopulationYE -0.18226 0.04054 155.23842 -4.496 1.35e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) PpltGI PpltKA PpltMA PpltMU PpltRE PpltUM PpltVA
## PopulatinGI -0.535
## PopulatinKA -0.606 0.443
## PopulatinMA -0.603 0.441 0.499
## PopulatinMU -0.605 0.442 0.500 0.500
## PopulatinRE -0.571 0.415 0.472 0.471 0.472
## PopulatinUM -0.590 0.429 0.488 0.486 0.487 0.460
## PopulatinVA -0.604 0.445 0.500 0.499 0.500 0.470 0.485
## PopulatinYE -0.606 0.443 0.501 0.499 0.500 0.472 0.488 0.500
anova(Via_lmer_Zwaan)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Population 1.1594 0.14492 8 151.78 6.6463 1.944e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summary(Via_lmer_Zwaan)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: ProportionEggtoAdultSurvival ~ Population + (1 | Line:Population) +
## (1 | Batch)
## Data: d_Via[d_Via$Supervisor.PI == "Zwaan", ]
##
## REML criterion at convergence: -328.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.8665 -0.4405 0.1045 0.5674 2.7223
##
## Random effects:
## Groups Name Variance Std.Dev.
## Line:Population (Intercept) 0.01052 0.1025
## Batch (Intercept) 0.00000 0.0000
## Residual 0.02180 0.1477
## Number of obs: 524, groups: Line:Population, 169; Batch, 2
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.753292 0.029612 148.129254 25.439 < 2e-16 ***
## PopulationGI -0.109677 0.045264 147.538931 -2.423 0.0166 *
## PopulationKA 0.006964 0.042022 149.533027 0.166 0.8686
## PopulationMA -0.039616 0.042055 150.208886 -0.942 0.3477
## PopulationMU -0.045938 0.042393 153.358912 -1.084 0.2802
## PopulationRE -0.107348 0.044247 145.966515 -2.426 0.0165 *
## PopulationUM 0.025615 0.043113 148.966551 0.594 0.5533
## PopulationVA -0.020760 0.042344 153.060112 -0.490 0.6247
## PopulationYE -0.226033 0.042298 153.374414 -5.344 3.24e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) PpltGI PpltKA PpltMA PpltMU PpltRE PpltUM PpltVA
## PopulatinGI -0.654
## PopulatinKA -0.705 0.461
## PopulatinMA -0.704 0.461 0.496
## PopulatinMU -0.698 0.457 0.492 0.492
## PopulatinRE -0.669 0.438 0.472 0.471 0.467
## PopulatinUM -0.687 0.449 0.484 0.484 0.480 0.460
## PopulatinVA -0.699 0.457 0.493 0.492 0.488 0.468 0.480
## PopulatinYE -0.700 0.458 0.493 0.493 0.489 0.469 0.481 0.490
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see ?isSingular
Contributors:
Schmidt Lab : Paul Schmidt
Contributors:
Gibert Lab : Cristina Vieira, Laurence Mouton, Natacha Kremer, Sonia Martinez, Patricia Gibert
Grath Lab : Ingo Müller, Sonja Grath
Hoedjes Lab : Hristina Kostic, Katja Hoedjes
Schmidt Lab : Ozan Kiratli, Yonatan Babore, Liam Forsythe, Paul Schmidt
Stamenkovic-Radak Lab : Marija Savic Veselinovic, Marija Tanaskovic, Aleksandra Patenkovic, Mihailo Jelic, Katarina Eric, Pavle Eric, Slobodan Davidovic, Marina Stamenkovic-Radak
Zwaan Lab : Joost van den Heuvel, Bas Zwaan
Contributors:
Colinet Lab : Sapho-Lou Marti , Hervé Colinet Hoedjes Lab : Hristina Kostic, Katja Hoedjes Onder Lab : Seda Coskun, Senel Selin Senkal, Dogus Can, Banu Sebnem Onder
Contributors:
Billeter Lab : Xiaocui Wang, Tiphaine Bailly, Mario Mira, Jean-Christophe Billeter
Fricke Lab : Claudia Fricke
Reading data in R
d_Fec <- read.csv("MasterSheets_Oct21_git/FEC_MasterSheet_Oct21.csv")
str(d_Fec)
## 'data.frame': 1725 obs. of 13 variables:
## $ Supervisor.PI : chr "Billeter" "Billeter" "Billeter" "Billeter" ...
## $ Diet : chr "NS" "NS" "NS" "NS" ...
## $ Batch : int 1 1 1 1 1 1 1 1 1 1 ...
## $ Population : chr "AK" "AK" "AK" "AK" ...
## $ Line : chr "AK1" "AK1" "AK1" "AK1" ...
## $ Individual : int 1 2 3 4 5 1 2 3 4 5 ...
## $ NumberOfAdultsEclosed: int 206 75 54 58 278 162 0 101 145 188 ...
## $ Notes : chr NA NA NA NA ...
## $ Censor : int 0 0 0 0 0 0 0 0 0 0 ...
## $ Country : chr "Finland" "Finland" "Finland" "Finland" ...
## $ Latitude : num 61.1 61.1 61.1 61.1 61.1 61.1 61.1 61.1 61.1 61.1 ...
## $ Longitude : num 23.5 23.5 23.5 23.5 23.5 ...
## $ Altitude : int 88 88 88 88 88 88 88 88 88 88 ...
Factors need reformatting (i.e. Supervisor.PI should be coded as a factor, not character).
str(d_Fec)
## 'data.frame': 1725 obs. of 13 variables:
## $ Supervisor.PI : chr "Billeter" "Billeter" "Billeter" "Billeter" ...
## $ Diet : chr "NS" "NS" "NS" "NS" ...
## $ Batch : int 1 1 1 1 1 1 1 1 1 1 ...
## $ Population : chr "AK" "AK" "AK" "AK" ...
## $ Line : chr "AK1" "AK1" "AK1" "AK1" ...
## $ Individual : int 1 2 3 4 5 1 2 3 4 5 ...
## $ NumberOfAdultsEclosed: int 206 75 54 58 278 162 0 101 145 188 ...
## $ Notes : chr NA NA NA NA ...
## $ Censor : int 0 0 0 0 0 0 0 0 0 0 ...
## $ Country : chr "Finland" "Finland" "Finland" "Finland" ...
## $ Latitude : num 61.1 61.1 61.1 61.1 61.1 61.1 61.1 61.1 61.1 61.1 ...
## $ Longitude : num 23.5 23.5 23.5 23.5 23.5 ...
## $ Altitude : int 88 88 88 88 88 88 88 88 88 88 ...
d_Fec$Supervisor.PI <- as.factor(d_Fec$Supervisor.PI)
d_Fec$Diet <- as.factor(d_Fec$Diet)
d_Fec$Batch <- as.factor(d_Fec$Batch)
d_Fec$Population <- as.factor(d_Fec$Population)
d_Fec$Population_Lat <- factor(d_Fec$Population, levels= c("YE","RE","GI","MU","MA","UM","KA","VA","AK"))
d_Fec$Population_Lon <- factor(d_Fec$Population, levels= c("RE","GI","KA","MU","MA","AK","UM","YE","VA"))
d_Fec$Population_Alt <- factor(d_Fec$Population, levels= c("KA","AK","GI","RE","UM","VA","MU","MA","YE"))
d_Fec$Line <- as.factor(d_Fec$Line)
d_Fec$NumberOfAdultsEclosed <- as.numeric(d_Fec$NumberOfAdultsEclosed)
d_Fec$Censor <- as.factor(d_Fec$Censor)
str(d_Fec)
## 'data.frame': 1725 obs. of 16 variables:
## $ Supervisor.PI : Factor w/ 2 levels "Billeter","Fricke": 1 1 1 1 1 1 1 1 1 1 ...
## $ Diet : Factor w/ 2 levels "NS","S": 1 1 1 1 1 1 1 1 1 1 ...
## $ Batch : Factor w/ 8 levels "1","2","3","4",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ Population : Factor w/ 9 levels "AK","GI","KA",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ Line : Factor w/ 169 levels "AK1","AK10","AK11",..: 1 1 1 1 1 11 11 11 11 11 ...
## $ Individual : int 1 2 3 4 5 1 2 3 4 5 ...
## $ NumberOfAdultsEclosed: num 206 75 54 58 278 162 0 101 145 188 ...
## $ Notes : chr NA NA NA NA ...
## $ Censor : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ Country : chr "Finland" "Finland" "Finland" "Finland" ...
## $ Latitude : num 61.1 61.1 61.1 61.1 61.1 61.1 61.1 61.1 61.1 61.1 ...
## $ Longitude : num 23.5 23.5 23.5 23.5 23.5 ...
## $ Altitude : int 88 88 88 88 88 88 88 88 88 88 ...
## $ Population_Lat : Factor w/ 9 levels "YE","RE","GI",..: 9 9 9 9 9 9 9 9 9 9 ...
## $ Population_Lon : Factor w/ 9 levels "RE","GI","KA",..: 6 6 6 6 6 6 6 6 6 6 ...
## $ Population_Alt : Factor w/ 9 levels "KA","AK","GI",..: 2 2 2 2 2 2 2 2 2 2 ...
d_Fec <- subset(d_Fec, Censor == "0")
Now they should be in the correct format, see below.
str(d_Fec)
## 'data.frame': 1721 obs. of 16 variables:
## $ Supervisor.PI : Factor w/ 2 levels "Billeter","Fricke": 1 1 1 1 1 1 1 1 1 1 ...
## $ Diet : Factor w/ 2 levels "NS","S": 1 1 1 1 1 1 1 1 1 1 ...
## $ Batch : Factor w/ 8 levels "1","2","3","4",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ Population : Factor w/ 9 levels "AK","GI","KA",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ Line : Factor w/ 169 levels "AK1","AK10","AK11",..: 1 1 1 1 1 11 11 11 11 11 ...
## $ Individual : int 1 2 3 4 5 1 2 3 4 5 ...
## $ NumberOfAdultsEclosed: num 206 75 54 58 278 162 0 101 145 188 ...
## $ Notes : chr NA NA NA NA ...
## $ Censor : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ Country : chr "Finland" "Finland" "Finland" "Finland" ...
## $ Latitude : num 61.1 61.1 61.1 61.1 61.1 61.1 61.1 61.1 61.1 61.1 ...
## $ Longitude : num 23.5 23.5 23.5 23.5 23.5 ...
## $ Altitude : int 88 88 88 88 88 88 88 88 88 88 ...
## $ Population_Lat : Factor w/ 9 levels "YE","RE","GI",..: 9 9 9 9 9 9 9 9 9 9 ...
## $ Population_Lon : Factor w/ 9 levels "RE","GI","KA",..: 6 6 6 6 6 6 6 6 6 6 ...
## $ Population_Alt : Factor w/ 9 levels "KA","AK","GI",..: 2 2 2 2 2 2 2 2 2 2 ...
Tables for descriptive statistics at population and line level can be found in the main trait directory, under the file name table_TraitAbbreviation_Level_BatchInfo.csv (i.e. table_Via_Line_wobatch.csv)
Descriptive statistics for viability at the line level, with batch information :
Descriptive statistics for fecundity at the line level, without batch information :
Descriptive statistics for viability at the population level, with batch information :
Descriptive statistics for viability at the population level, without batch information :
The very same theme has been used all across the document, being a ggplot theme called droseu_theme
droseu_theme <- theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(), panel.background = element_blank(), axis.line = element_line(colour = "black",), axis.title.x = element_text(size = 16), axis.text.x = element_text(size = 16),axis.text.y = element_text(size = 16),axis.title.y = element_text(size = 16))
Data range is calculated with #r min() and #r max() functions for each trait.
min_Fec <- min(d_Fec$NumberOfAdultsEclosed)
max_Fec <- max(d_Fec$NumberOfAdultsEclosed)
y-axis is scaled by the minimum (0) and maximum (306) values in the full data set.
Here is an example code for fecundity, Billeter Lab. The same code is used to generate plots for other contributing labs by filtering the data at supervisor level (for females and males, if applicable).
pdf(file="Fecundity/p_Fec_Billeter.pdf",width=8, height=5)
p_Fec_Billeter <- ggplot(data = (subset(d_Fec,Supervisor.PI=='Billeter')), aes(x=Population_Lat, y=NumberOfAdultsEclosed, fill=Batch)) + geom_boxplot(outlier.shape = NA, notch=FALSE) + geom_jitter(width=0.2,alpha=0.05) + labs(title="p_Fec_Billeter", x="Population", y = "NumberOfAdultsEclosed")
p_Fec_Billeter + coord_cartesian(ylim = c(min_Fec, max_Fec))+ droseu_theme
dev.off()
## pdf
## 3
pdf(file="Fecundity/p_Fec_pop_Billeter.pdf",width=8, height=5)
p_Fec_pop_Billeter <- ggplot(data = (subset(d_Fec,Supervisor.PI=='Billeter')), aes(x=Population_Lat, y=NumberOfAdultsEclosed, fill=Population_Lat)) + geom_boxplot(outlier.shape = NA, notch=FALSE) + geom_jitter(width=0.2,alpha=0.05) + labs(title="p_Fec_pop_Billeter", x="Population", y = "NumberOfAdultsEclosed")
p_Fec_pop_Billeter + coord_cartesian(ylim = c(min_Fec, max_Fec))+ droseu_theme
dev.off()
## pdf
## 3
Fec_lmer_Billeter <- lmer(NumberOfAdultsEclosed ~ Population + (1|Population:Line), data = d_Fec[d_Fec$Supervisor.PI == "Billeter",])
capture.output(anova(Fec_lmer_Billeter),file = "Fecundity/Fec_lmer_Billeter.txt")
capture.output(summary(Fec_lmer_Billeter),file = "Fecundity/Fec_lmer_Billeter_sum.txt")
capture.output(emmeans(Fec_lmer_Billeter, list(pairwise ~ Population), adjust = "tukey"),file = "Fecundity/Fec_lmer_Billeter_tukey.txt")
anova(Fec_lmer_Billeter)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Population 61874 7734.2 8 148.39 2.7992 0.006461 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summary(Fec_lmer_Billeter)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: NumberOfAdultsEclosed ~ Population + (1 | Population:Line)
## Data: d_Fec[d_Fec$Supervisor.PI == "Billeter", ]
##
## REML criterion at convergence: 8773.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.02323 -0.55643 -0.03579 0.53645 2.97008
##
## Random effects:
## Groups Name Variance Std.Dev.
## Population:Line (Intercept) 1113 33.36
## Residual 2763 52.56
## Number of obs: 805, groups: Population:Line, 160
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 97.067 9.154 149.480 10.603 <2e-16 ***
## PopulationGI -22.874 13.904 146.434 -1.645 0.1021
## PopulationKA 12.903 12.928 148.886 0.998 0.3198
## PopulationMA 2.053 13.460 147.925 0.152 0.8790
## PopulationMU -3.225 13.226 152.369 -0.244 0.8077
## PopulationRE -29.213 14.231 148.404 -2.053 0.0419 *
## PopulationUM 5.760 13.959 148.978 0.413 0.6805
## PopulationVA -10.734 12.894 147.238 -0.832 0.4065
## PopulationYE -33.130 12.963 150.368 -2.556 0.0116 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) PpltGI PpltKA PpltMA PpltMU PpltRE PpltUM PpltVA
## PopulatinGI -0.658
## PopulatinKA -0.708 0.466
## PopulatinMA -0.680 0.448 0.482
## PopulatinMU -0.692 0.456 0.490 0.471
## PopulatinRE -0.643 0.424 0.455 0.437 0.445
## PopulatinUM -0.656 0.432 0.464 0.446 0.454 0.422
## PopulatinVA -0.710 0.467 0.503 0.483 0.491 0.457 0.466
## PopulatinYE -0.706 0.465 0.500 0.480 0.489 0.454 0.463 0.501
## pdf
## 3
## pdf
## 3
anova(Fec_lmer_Fricke)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Population 4506.3 563.29 8 146.12 0.4525 0.8873
summary(Fec_lmer_Fricke)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: NumberOfAdultsEclosed ~ Population + (1 | Population:Line) +
## (1 | Batch)
## Data: d_Fec[d_Fec$Supervisor.PI == "Fricke", ]
##
## REML criterion at convergence: 9249.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.8102 -0.6481 -0.0705 0.5894 3.4523
##
## Random effects:
## Groups Name Variance Std.Dev.
## Population:Line (Intercept) 455.06 21.332
## Batch (Intercept) 41.26 6.423
## Residual 1244.86 35.283
## Number of obs: 916, groups: Population:Line, 161; Batch, 8
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 61.074 6.779 95.724 9.009 2.05e-14 ***
## PopulationGI -10.583 9.374 152.556 -1.129 0.261
## PopulationKA -10.298 8.769 143.094 -1.174 0.242
## PopulationMA -5.089 8.673 146.815 -0.587 0.558
## PopulationMU -4.207 8.875 147.002 -0.474 0.636
## PopulationRE -3.612 9.302 151.133 -0.388 0.698
## PopulationUM -14.075 8.963 148.011 -1.570 0.118
## PopulationVA -7.192 8.825 149.162 -0.815 0.416
## PopulationYE -8.232 8.793 151.818 -0.936 0.351
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) PpltGI PpltKA PpltMA PpltMU PpltRE PpltUM PpltVA
## PopulatinGI -0.623
## PopulatinKA -0.665 0.483
## PopulatinMA -0.671 0.486 0.522
## PopulatinMU -0.667 0.475 0.511 0.513
## PopulatinRE -0.628 0.455 0.487 0.495 0.478
## PopulatinUM -0.653 0.472 0.505 0.514 0.495 0.479
## PopulatinVA -0.668 0.477 0.513 0.521 0.510 0.484 0.503
## PopulatinYE -0.668 0.480 0.514 0.522 0.507 0.486 0.506 0.511
Meeting w Eran and Chris
Eran to make circular figures for both CET and LA